Tata Motors Limited DCF Valuation

TMCV · Capital Goods · Current price ₹420.2

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DocStoX fair value
₹224
medium confidence · 67/100
Current price
₹435
Upside to fair value
-48.5%
Estimate range
₹202 – ₹246
Cash-flow model
₹233
Discounted cash flow (2-stage) · 60% weight
5Y profit growth
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Tata Motors Limited cash-flow valuation

Our discounted cash flow (2-stage) values Tata Motors Limited at ₹233 per share. Intrinsic value from 2-stage discounted cash flows (primary for asset-light / normal businesses); FCFE path. It carries 60% of the weight in our published estimate of ₹224. A discounted cash flow is only as good as the cash-flow path it assumes, so the assumptions behind it are listed in full below.

Try your own assumptions

Prefer to build the discounted cash flow yourself? The DCF calculator below opens pre-filled with Tata Motors Limited's reported free cash flow, net debt and share count, so you can set your own growth and discount rate.

How we got this fair value

Every number below is part of the same estimate for Tata Motors Limited. Nothing here is a target price or a forecast of where the share price will go.

Medium confidence67/100

Workable, with real gaps. Weigh it alongside your own view rather than on its own.

Estimate range ₹202 – ₹246

Why this method for this business

Tata Motors is a capital-intensive business with cyclical earnings, making DCF and EV/EBITDA the most appropriate models. Relative PE is used to anchor the valuation against peers.

The models behind the number

Each method is weighted by how well its inputs held up for this company.

Discounted cash flow (2-stage)
₹23360% weight

Intrinsic value from 2-stage discounted cash flows (primary for asset-light / normal businesses); FCFE path

Fair multiple (P/E)
₹19810% weight
EV / EBITDA(not enough data)

required inputs missing

  • Discounted cash flow (2-stage) is the primary model due to its alignment with capital-intensive and cyclical businesses. EV/EBITDA is used to complement DCF. Relative PE is used for peer comparison.

What was missing

  • Lack of historical P/E data for peer comparison
  • Limited forward estimates for EBITDA margins

What drives it

26 inputs, grouped by how strong each claim is
ReportedRead from filings or market data. Verifiable, not our opinion.
FCF_0 (normalized)
8,153.5 ₹cr

fcf_norm (median ≤5y positive)

Net debt
5,615 ₹cr

gross debt (cash n/a)

Sector-median P/E
21.81×

peer-median

Peers used
76

peer-median

EPS (TTM)
8.23 ₹/share

computed

MacroEconomy-wide input, not company-specific.
Discount rate (r)
15.53%

COE (building-block)

Beta
1.15

bottom-up industry

AssumptionA forward input the model chose. Reasonable people can disagree here.
Stage-1 start growth
5.5%

pat_cagr_5y

Fade shape
linear

lifecycle Mature

Terminal growth (g)
5.5%

macro terminal × quality premium

Stage-1 years
14 y

lifecycle Mature horizon

JudgementOur read of the evidence, not a measurement.
Lifecycle stage
Mature

forecast engine (L20)

Quality premium
1.1×

moat Wide + capital allocation

Term: quality
+7.1%

quality 69/100 (high, stable returns) → premium

Term: growth
-11.4%

weak ~3% CAGR (ROCE 36% > hurdle) → discount

Term: capital_allocation
+6.0%

capital allocation: rising ROCE funded by strong FCF → premium

Term: financial_strength
+8.4%

financial strength: low leverage (D/E 0.3) → mild premium

Term: history
+0.0%

history: no reliable own-history P/E → not used

Term: sector_rerating
+0.0%

sector re-rating: no sector-norm history → not used

Term: rate_regime
+1.2%

rates: risk-free 6.9% below ~7.1% normal → higher multiple

Term: risk
+0.0%

risk: no analyst-dispersion signal available → neutral (lowers confidence, not PE)

DerivedFalls out of the inputs above: a model output, not an independent input.
Path
FCFE

computed (D/E vs 0.8)

Terminal-value fraction
0.281

PV_TV / (PV_1+PV_TV)

Premium vs sector
+10.5%

fundamentals + macro (not capped at own history)

Fair-multiple confidence
44%

data completeness + term agreement

Fair P/E
24.09×

sector median × ∏(1+term) — Dynamic Fair Multiple

Who computed this

A language model (qwen3:8b) selects the valuation method and the judgement inputs for Tata Motors Limited: which models suit the business, the moat, the growth and margin assumptions. It does not produce the number. The fair value itself is computed in Python by a deterministic engine (version 2026.08.1) from those inputs, so the same inputs always give the same result and every figure above can be traced to a source.

Estimated 14 Sept 2026. Re-run as new financials and news arrive.

How we value stocks

We estimate what a business is worth from its own economics: cash flows, returns on capital, and a multiple justified by its quality, rather than from where the share price has been. A fair value estimate is a view, not a verdict, and it is only as good as the inputs listed above.

Read the full fair-value methodology

More on Tata Motors Limited

DocStoX estimates are computed from reported financials, for informational purposes only, not investment advice. Consult a SEBI-registered advisor before investing.